A feature frequency analysis program has been written which tabulates features for groups of organisms and the uniqueness of each feature for each group. The uniqueness of a feature for organisms from a specific disease may be determined. Ranking the features by their mean deviations gives their ability to partition the groups most efficiently. We are developing an algorithm for displaying the level of redundancy of features to aid the microbiologist in choosing the "best" set of features useful in identification by probabilities. By appropriate redesign of numerical taxonomic programs, their versatility and cost effectiveness have been greatly enhanced. The similarity calculations may be used to develop group statistics using taxonomic and any other logic. This allows such direct comparisons as taxonomically derived groups with groups based on disease state from which isolated and, thus, detections of organisms associated with particular disease states.